Decoupling Capacitor Selection Algorithm for PDN Based on Deep Reinforcement Learning

Selection of decoupling capacitors (decaps) is important for power distribution network (PDN) design in terms of lowering impedance and saving cost. Good PDN designs typically mean satisfying a target impedance with as less decaps as possible. In this paper, an inductance-based method is utilized to calculate the port priority fist, and afterwards deep reinforcement learning (DRL) with deep neural network (DNN) is applied to optimize the assignment of decaps on the prioritized locations. The DRL algorithm can explore by itself without any prior physical knowledge, and the DNN is trained with the exploration experience and eventually converges to an optimum state. The proposed hybrid method was tested on a printed-circuit-board (PCB) example. After some iterations of training the DNN successfully reached to an optimum design, which turned out to be the minimum number of decaps that can satisfy the target impedance. The usage of DRL with DNN makes the algorithm promising to include more variables as input and handle more complicated cases in the future.

I. Introduction

With the requirement of modern high-speed digital systems on higher currents and lower supply voltages, power distribution network (PDN) is becoming increasingly significant, because it controls the fluctuations of the power supply and affects the stability of integrated circuits (ICs) [1] [2]. Decoupling capacitor (decap) is one of the most important components in PDN design to satisfy target impedance. The minimum number of decaps is desired in PDN system design to save cost and layout space and satisfy a target impedance at the same time.